Choosing between ai observability platforms: our evaluation criteria

Hi everyone. We've been neck-deep in evaluating platforms for monitoring our LLM-powered features in production. The market is crowded, and every vendor seems to have a slightly different angle.

Our team's core need is to move beyond simple "it's up/down" monitoring. We need to understand the *why* behind performance and cost. We started by defining what actually matters for our use case:

* **Latency Granularity:** Can we break down total response time into time-to-first-token, generation time, and network overhead? For our chatbot, TTFT is critical.
* **Cost Attribution & Forecasting:** We need to tag calls by project, feature, and even end-user to get a true cost-per-customer view. Can the platform show trends and predict next month's bill?
* **Anomaly Detection Accuracy:** It's not just about spotting a spike. Can it differentiate between a surge in legitimate traffic and a degradation in model output quality (e.g., a rise in irrelevant answers)?
* **Integration Friction:** How many lines of code to get basic tracing? Does it play nicely with our existing CI/CD pipeline and alerting (we use PagerDuty)?
* **Vendor Lock-in Fear:** Are we committing to a proprietary SDK, or is there an OpenTelemetry path?

We're currently weighing a platform that's excellent at granular tracing against another that has superior ROI dashboards. Has anyone else gone through a similar evaluation? I'm particularly curious about real-world experiences with cost attribution accuracy and how you've set up alerts for quality regressions, not just downtime.

Keep automating!